Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes far beyond the readOnly/idempotent/destructive annotations. It discloses source gap-fill behavior, the merged top_holders semantics and false-merge detection, the special meaning of holder_count_divergence, and that the summary narrative runs on a separate cadence and may disagree with live counts. These are exactly the non-obvious behavioral traits an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and long, but nearly every sentence adds unique operational or interpretation detail that justifies its length. It is front-loaded with the core purpose and signal semantics. It could be improved with clearer paragraph or bullet separation, but it is not padded or redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the presence of an output schema, the description explains the nuanced parts of the response that a schema alone cannot: source merging, the systemic-multiplier condition behind holder_count_divergence, and the trust hierarchy when summary disagrees with counts. It also flags Pro-tier cost. For a single-parameter, read-only data tool, this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single `symbol` parameter is already fully documented by the schema. The description does not add validation, format, or syntax details beyond 'for a stock.' Baseline 3 is appropriate because the schema carries the parameter documentation burden and the description adds no meaningful input semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Get AI-synthesized insider + institutional activity for a stock.' It clearly delimits the tool's scope from sibling market-data tools by describing the combined insider/holdings signal and its exact output components. The purpose is unambiguous and distinct even without comparing against sibling names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: use this when you need insider transactions, institutional holdings, and a combined signal for a single stock. It also adds operational guidance like 'Pro tier only — AI pipeline cost attached.' It does not explicitly name alternatives or state when not to use it, but the context is strong enough to imply appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.